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With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.
Sichkariuk et al. (Thu,) studied this question.